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ModelCraft: an advanced automated model-building pipeline using Buccaneer.

Paul S Bond1, Kevin D Cowtan1

  • 1Department of Chemistry, University of York, York YO10 5DD, United Kingdom.

Acta Crystallographica. Section D, Structural Biology
|September 1, 2022
PubMed
Summary

ModelCraft is a new automated pipeline that improves protein model completeness in structural biology. It enhances automated model building, especially for challenging datasets, leading to more accurate structural solutions.

Keywords:
BuccaneerModelCraftX-ray crystallographyautomationmodel buildingsoftwarestructure solution

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Area of Science:

  • Structural Biology
  • Computational Biology
  • Biophysics

Background:

  • Automated model building aids protein structure determination but can struggle with low-resolution data or poor initial models.
  • Existing pipelines like Buccaneer, while useful, have limitations in handling complex or low-quality structural data.
  • The structure-solution process often requires iterative refinement and correction for accurate molecular models.

Purpose of the Study:

  • To develop and evaluate ModelCraft, an advanced automated pipeline for protein model building in structural biology.
  • To improve the completeness and accuracy of protein models generated from various structure solution methods.
  • To overcome limitations of previous automated methods, particularly with challenging datasets.

Main Methods:

  • ModelCraft integrates automated model building with advanced techniques like shift-field refinement and machine-learned residue pruning.
  • The pipeline incorporates density modification, water and dummy atom addition, nucleic acid building, and side-chain rebuilding.
  • Performance was assessed using large datasets from experimental phasing, molecular replacement with homologues, and molecular replacement with AlphaFold models.

Main Results:

  • ModelCraft significantly increased mean protein model completeness across all tested datasets compared to the Buccaneer pipeline.
  • Completeness improved from 91% to 95% in experimental phasing cases.
  • In molecular replacement cases, completeness rose from 50% to 78% (homologues) and 82% to 91% (AlphaFold models).

Conclusions:

  • ModelCraft represents a substantial advancement in automated model building for protein structure determination.
  • The pipeline demonstrates superior performance, especially with lower-resolution data and diverse structure solution approaches.
  • This tool enhances the efficiency and success rate of obtaining complete and accurate protein models.